Schema Architect

Schema Architect

Design and implement Schema.org structured data for Google Search rich results, AI Overviews, and classic indexing.

Routing:

• Use this skill for Google rich results, AI Overviews eligibility, and entity disambiguation in classic search.
• Use ai-crawler-readiness for HTTP-layer LLM visibility in ChatGPT, Claude, Perplexity, and Gemini chat.
• Use llms-txt-builder for AI navigation file content.

Caveat: Structured data helps Google's classic indexing and AI Overviews via search. There is no public evidence that current chat LLMs parse JSON-LD when answering, so do not treat schema as the primary citation lever.

Schema Priority Matrix

High-Impact (Implement First):

Medium-Impact: Person, LocalBusiness, Event, Course, SoftwareApplication

Site Archetype Mappings

See references/site-archetypes.yaml for page-to-schema mappings per site type (SaaS, e-commerce, agency, blog, docs).

Implementation Templates

See references/schema-templates.yaml for JSON-LD code snippets:

• Organization schema
• FAQPage schema
• HowTo schema
• Article schema
• Product schema

Multimodal Structured Data

Include media references:

• Image objects with alt descriptions
• Video objects with transcripts
• Audio references where applicable

Workflow

1. Audit existing structured data
2. Identify missing high-impact schemas
3. Map content types to schema types
4. Generate JSON-LD code
5. Validate with testing tools
6. Plan maintenance schedule

Output Format

Schema Implementation Plan
--------------------------
Current Coverage: X/10
Target Coverage: Y/10

Priority Implementations:
1. Organization schema (homepage)
2. FAQPage schema (support pages)
3. Article schema (blog posts)
4. HowTo schema (tutorials)

Validation:
- Rich Results Test: [Pass/Fail]
- Schema.org Validator: [Pass/Fail]

Validation Tools

• Google Rich Results Test
• Schema.org Validator
• Structured Data Linter

Platform Integration